Human attachments shape interbrain synchrony toward efficient performance of social goals
Bibliographic record
Abstract
The human brain has undergone massive expansion across primate evolution through life amidst multi-layered social attachments; within families, among friends, and between clan members and this enabled humans to coordinate their brains with those of others toward the execution of complex social goals. We examined how human attachments facilitate efficient, resource-sensitive performance of social goals by balancing neural and behavioral synchrony. Using hyperscanning EEG, we collected neural data from male-female pairs in three groups (N=158, 79 pairs); long-term couples, best friends, and unfamiliar group members, during two ecologically-valid naturalistic tasks; motor coordination and empathy giving. Across groups and tasks, neural synchrony was supported by behavior coordination and orchestrated multiple neural rhythms. In the goal-directed motor task, interbrain synchrony implicated beta and gamma rhythms localized to sensorimotor areas. Couples showed the highest neural synchrony combined with greatest behavioral synchrony and such brain-behavior linkage resulted in speedy performance, conserving energy in the long run. The socially-oriented empathy task triggered neural synchrony in widely-distributed sensorimotor and bilateral temporal regions, integrated alpha, beta, and gamma rhythms, and implicated brain-behavior complementarity; couples displayed the highest behavioral synchrony combined with lowest neural synchrony toward greatest felt support while strangers exhibited the opposite pattern. Findings suggest that human attachments provide a familiar backdrop of temporal regularities, required for the brain's allostatic function, and interbrain and behavioral synchrony are sculpted by familiarity and closeness toward resource-sensitive performance of survival-related social goals, toiled by two.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".